Reliable epidemiological reasoning requires synthesizing study evidence to infer disease burden, transmission dynamics, and intervention effects at the population level. Existing medical question answering benchmarks primarily emphasize clinical knowledge or patient-level reasoning, yet few systematically evaluate evidence-grounded epidemiological inference. We present EpiQAL, the first diagnostic benchmark for epidemiological question answering across diverse diseases, comprising three subsets built from open-access literature. The three subsets progressively test factual recall, multi-step inference, and conclusion reconstruction under incomplete information, and are constructed through a quality-controlled pipeline combining taxonomy guidance, multi-model verification, and difficulty screening. Experiments on fifteen models spanning open-source and proprietary systems reveal that current LLMs show limited performance on epidemiological reasoning, with multi-step inference posing the greatest challenge. Model rankings shift across subsets, and scale alone does not predict success. Chain-of-Thought prompting benefits multi-step inference but yields mixed results elsewhere. EpiQAL provides fine-grained diagnostic signals for evidence-grounding, inferential reasoning, and conclusion reconstruction.
@article{arxiv.2601.03471,
title = {EpiQAL: Benchmarking Large Language Models in Epidemiological Question Answering and Reasoning},
author = {Mingyang Wei and Dehai Min and Zewen Liu and Yuzhang Xie and Guanchen Wu and Ziyang Zhang and Carl Yang and Max S. Y. Lau and Qi He and Lu Cheng and Wei Jin},
journal= {arXiv preprint arXiv:2601.03471},
year = {2026}
}